What Can We Learn From the FDA Model for AI Regulation? - AI Now Institute

Where AI Regulation Stands Today

The White House has released a National Artificial Intelligence Legislative Framework and new executive orders aiming to establish a single, nationwide standard for AI regulation...

Pacing the Frontier | Gillian K. Hadfield

The Pacing the Frontier letter calls on the US government to support an international effort to build the technical and governance tools needed to protect our option to pace AI development. I and others have been working on the problem of how to build such infrastructure for ten years, including participating in dialogues on AI safety with Chinese academic colleagues during the past three. Here are my suggestions: 1. Don’t rely on off-the-shelf models like FINRA and the FDA which were built for 20th Century single-domain government expertise. They’re not fit for purpose. 2. Don’t act like no-one’s thought about the AI governance problem before. We’ve spent two years refining a regulatory markets design into working legislative language, for example, and it’s now in AI governance bills in five states and in Congress. 3. Don’t try to write an exhaustive set of rules for AGI first.  4. Pick a domain that can achieve widespread global consensus to start. Mine would be recursive self-improvement: models should not build models. Build the technology that verifies that. 5.  Focus relentlessly on building flexible verification infrastructure that is able to enforce whatever rules we can ultimately agree on.  6. Don’t assume we already know how to do this and governments can just write tests into law. Technology needs to be built and by the private sector. 7. Don’t wait for the infrastructure to emerge first. The components and people are there and the ecosystem can scale fast with the right incentives. 8. Incentivize large-scale investment in verification technology by building a governance structure and industry funding that creates a market for private verification organizations. 9. Use licensing and public oversight to ensure verifiers are independent of the frontier labs. 10. Protect sovereignty by enabling each government to license its own verifiers from a global market of verifiers recognized by other countries. 11. Leverage the incentive of global trade for models and model services by requiring verification for market access. 12. Just start. Sources in comments.

Frontier AI Regulation Blueprint

A high-level blueprint for domestic regulation of civilian advanced AI models

AI's Affordability Crisis

A year ago in The Back Of The AI Envelope I pointed out that the AI platforms were running the drug-dealer's algorithm, "the first one's fr...

The EU AI Act Is Ready – Interdependent Thoughts

A final draft of the European AI Regulation is circulating (here’s an almost 900 page PDF). The coming days I will read it with curiosity.

OpenAI Calls for New Industrial Policy as AI Reshapes Economy and Governance

OpenAI’s policy paper argues that governments must adopt a new industrial policy to manage AI’s economic disruption.

AI Act Scientific Panel

The Scientific Panel advises the AI Office and national authorities on the implementation the AI Act and assessment of the impacts and risks of General-Purpose AI (GPAI) models.

The Risks of Industry Influence in Tech Research

Emerging information technologies like social media, search engines, and AI can have a broad impact on public health, political institutions, social dynamics, and the natural world. It is critical to develop a scientific understanding of these impacts to inform evidence-based technology policy that minimizes harm and maximizes benefits. Unlike most other global-scale scientific challenges, however, the data necessary for scientific progress are generated and controlled by the same industry that might be subject to evidence-based regulation. Moreover, technology companies historically have been, and continue to be, a major source of funding for this field. These asymmetries in information and funding raise significant concerns about the potential for undue industry influence on the scientific record. In this Perspective, we explore how technology companies can influence our scientific understanding of their products. We argue that science faces unique challenges in the context of technology research that will require strengthening existing safeguards and constructing wholly new ones.

Who decides when AI is too dangerous?

Anthropic asked for AI regulation, but not like this.

Import AI 459: AI oversight is difficult; scaling laws for protein folding models; and pricing the extinction risk of AI systems

Do you feel as though you are living in a revolution?

Responsible Innovation at the Frontier - Americans for Responsible Innovation

ARI’s blueprint for federal AI governance is designed to promote safe frontier AI development in America. The blueprint is built around three governance functions any federal proposal should incorporate.

Everybody needs a personal AI policy. Just ask Hank Green.

How can we reap AI’s benefits without melting our brains in the process?

Americans for Responsible Innovation on Twitter / X

NEW: Today ARI released a blueprint for federal AI governance, including three pillars that promote safe frontier AI development: ✅ Standards set by the government ✅ Independent assurance they are met ✅ Transparency into frontier AI development https://t.co/s2mlUP27Wm pic.twitter.com/X6IhBS9jKF— Americans for Responsible Innovation (@americans4ri) August 10, 2026